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Leah Ding Assoc Professor Department of Computer Science

Send email to Leah Ding
CAS - Computer Science
Dr. Ding is broadly interested in cybersecurity and data privacy with a recent focus on the intersections between security, privacy, and machine learning. On one hand, she leverages machine learning techniques to study security and privacy. On the other hand, she studies the trustworthiness of machine learning models, and investigates how to build secure and privacy-preserving machine learning models. She is also interested in wireless and IoT security, privacy, and forensics.

She has extensive experience doing cybersecurity R&D in industrial research labs. Prior to joining AU, she was a Research Principal at Accenture Labs (the R&D division of Accenture), and an adjunct professor at Johns Hopkins University.
For the Media
To request an interview for a news story, call AU Communications at 202-885-5950 or submit a request.


Spring 2022

  • CSC-316 Computer Science III

Fall 2022

  • CSC-316 Computer Science III

  • CSC-480 Introduction to Data Mining

Scholarly, Creative & Professional Activities

Selected Publications

"Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception," R. Duan, Z. Qu, S. Zhao, L. Ding, Y. Liu, Z. Lu, the ACM Conference on Computer and Communications Security (CCS) (ACM CCS 2022), Nov. 2022.

"A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements," S. Das, Y. Wang, J. Gong, L. Ding, S. J. Munchak, C. Wang, D. L. Wu, L. Liao, W. S. Olson, D. O. Barahona, Remote Sensing (Journal), Volume 14, Issue 15, Jul. 2022.

"ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles," X. Yuan, L. Ding, L. Zhang, X. Li and D. O. Wu, IEEE Transactions on Emerging Topics in Computational Intelligence, Mar. 2022.

"Adversarial Email Generation Against Spam Detection Models Through Feature Perturbation," Q. Cheng, A. Xu, X. Li, L. Ding, IEEE International Conference on Assured Autonomy (ICAA), Mar. 2022.

"Defending against GAN-based DeepFake Attacks via Transformation-aware Adversarial Faces," C. Yang, L. Ding, Y. Chen, H. Li, the International Joint Conference on Neural Networks (IJCNN), Jul. 2021.

"Crafting Adversarial Email Content against Machine Learning Based Spam Email Detection," C. Wang, D. Zhang, S. Huang, X. Li, and L. Ding, in Proceedings of the 2021 International Symposium on Advanced Security on Software and Systems (ASSS '21) with AsiaCCS, Jun. 2021.

"Are Smart Home Devices Abandoning IPV Victims?" A. Alshehri, M. B. Salem, L. Ding, IEEE TrustCom/C4W, Dec. 2020. [Link]

"Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks," X. Yuan, L. Ding, M. B. Salem, X. Li, D. Wu, SecureComm, Oct. 2020. [Link]

"A Novel Architecture for Automatic Document Classification for Effective Security in Edge Computing Environments," L. Ding, M. B. Salem, IEEE/ACM Symposium on Edge Computing/EdgeSP, Oct. 2018. [Link]

Professional Services

Co-chair, the Forth ACM/IEEE Workshop on Security and Privacy in Edge Computing, Dec 2021. [Link]

Co-chair, IEEE International Workshop on Quantum Communication and Quantum Cryptography, Oct. 2021. [Link]

Local Chair, the IEEE/ACM International Conference on Connected Health Applications, Systems, and Engineering Technologies (CHASE), Dec. 2021. [Link]

Publicity co-chair, the IEEE Conference on Communications and Network Security (CNS), Oct. 2021. [Link]

Co-chair, the Third ACM/IEEE Workshop on Security and Privacy in Edge Computing, Nov. 2020. [Link]

Orgnazier and Panel Moderator, Women-in-Computing Forum of the ACM/IEEE Symposium on Edge Computing, Nov. 2019. [Link]

Professional Presentations

"Trust Preservation in the Age of AI," invited talk, Women in Hardware and Systems Security(WISE) workshop, Dec. 2020. [Link]

"Hype or hope? Machine learning based security analytics for web applications," L. Ding, X. Yuan, M. B. Salem, Annual Computer Security Applications Conference (ACSAC)/Case Studies, Dec. 2019. [Link]

"Automated REST API Endpoint Identification for Security Testing at Scale: How Machine Learning Accelerates Security Testing," L. Ding, J. Jacob, J. Chen, S. Pham, Blackhat Asia, Mar. 2019. [Link]


  • Congratulations to our labbers Lexie Rista (BS - Computer Science), Archibald Latham (BS - Computer Science), and Huong Doan (MS - Data Science) on being the recipients of the 31st annual Robyn Rafferty Mathias Student Research Conference Awards! (April 2021) [link]